The present invention relates generally to a method and an apparatus for modeling and predicting traffic patterns and, in particular, to a method and an apparatus for modeling and predicting traffic patterns for a group of elevators.
To date, elevator traffic modeling schemes have made wide use of queuing theory, based primarily on the Poisson distribution, to model the arrival of passengers at floors served by the elevators. Schemes have been proposed which use a single arrival rate for a whole building or an arrival rate which is unique to each individual floor. These schemes are based on the fundamental assumption that the chosen arrival rates remain unchanged throughout the daily and longer term life of the building. However, this assumption is invalid in modern buildings having smaller floor populations, where the movement of floor occupants can significantly affect their arrival rate at the elevator entrances as well as their destinations. Secondly, building usage can change significantly throughout its lifetime and, accordingly, so might the arrival rate behavior of its occupants. Finally, the poisson distribution is only regarded as an approximation to queuing behavior in an elevator context.
Recent traffic modeling schemes have attempted to solve some of the above described shortcomings in schemes utilizing queing theory by employing techniques which build tables of statistics representing important traffic events. New events are predicted and added to these tables using parameterized exponential smoothing functions. These systems only provide for discrete events, and the exponential smoothing techniques may lose valuable information. As such, statistical techniques which extrapolate their predictions from current and historical traffic events have been known for many years and can also be considered as "Artificial Intelligence". However, two general comments on these statistical techniques are appropriate: a prior interpretation of the data is often required, and subtle effects of variables on observed traffic behavior are often difficult, if not impossible, to represent.
An "Artificial Intelligence" based crowd sensing system for elevator car assignment is shown in the European patent application no. 0 385 811. In the method proposed in this patent application, observations are classified as "interesting" before they are stored or any other action taken. For example, "interesting" could be classified as two cars stopping at a floor within three minutes of each other. Such an approach relies upon the classification of "interesting" being appropriate for most events. The criteria which specify an "interesting" event are fixed and, therefore, may not be appropriate for all elevator installations. Future events are extrapolated from recent events, which are combined using an exponential smoothing technique. Long-term events are predicted from a long-term data base. Only events which are deemed to be "interesting" are considered for addition to the long term data base. After addition, events are again combined using exponential smoothing techniques. Such an approach appears to be inflexible and capable of representing only large scale events. The present invention seeks to provide a remedy for such problems and deficiencies.